As part of the Urban Analytics Data Dive at the Alan Turing Institute, I have recently spent two days working on data science solutions to public policy problems in an interdisciplinary team. We focused on identifying potential sites for the construction of new homes within cities by combining satellite data images, an urban atlas, planning applications, and other data sets. I am excited to say that our team was awarded the second prize by the jury.
Being an astronomer, my idea was to identify brownfield sites - i.e. previously developed but now disused industrial sites - based on their spectral signature in Sentinel-2 satellite images. Using an existing pilot register of brownfield sites in the Greater Manchester area and the satellite data, I have trained a classification algorithm. This classifier could then be applied to identify candidate sites in other cities. While the relatively narrow time frame of the data dive did not leave enough time to turn this idea into production, I have written up my ideas and a first demonstration here.